Organizations struggle to leverage sensitive data for machine learning and generative AI initiatives due to privacy regulations, security concerns, and the risk of exposing proprietary information. Traditional privacy approaches often reduce model accuracy or inject noise, limiting the potential for valuable insights.

Inpher provides privacy-preserving AI and machine learning solutions that enable organizations to analyze sensitive data without transferring or exposing it. Their SecurAI platform allows for secure and compliant use of generative AI, ensuring that proprietary data remains private while enhancing predictive model accuracy. By utilizing technologies like Secure Multiparty Computation and Fully Homomorphic Encryption, Inpher unlocks data across organizational silos, enabling secure data collaboration and more accurate business insights. The platform allows users to generate prompts to a model securely and without a third-party, permitting users to generate inference off of their own private or proprietary data from a number of sources without risk of exposure to the model server.

Secure Multiparty Computation (SMPC) for collaborative analytics without data transfer

Fully Homomorphic Encryption (FHE) for performing computations on encrypted data

SecurAI platform for secure and compliant generative AI applications

Encryption-in-use technology to maintain data privacy during processing

Secure prompt generation for large language models (LLMs) without third-party exposure

Attestation features to ensure the integrity and security of the AI environment

Compatibility with NVIDIA Confidential Computing for enhanced security

Inpher's primary customers are organizations in industries such as finance, healthcare, and government that handle sensitive data and require secure AI and machine learning solutions.